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Type I error rates, coverage of confidence intervals, and variance estimation in propensity-score matched analyses.
Int J Biostat. 2009 Apr 14; 5(1):Article 13.IJ

Abstract

Propensity-score matching is frequently used in the medical literature to reduce or eliminate the effect of treatment selection bias when estimating the effect of treatments or exposures on outcomes using observational data. In propensity-score matching, pairs of treated and untreated subjects with similar propensity scores are formed. Recent systematic reviews of the use of propensity-score matching found that the large majority of researchers ignore the matched nature of the propensity-score matched sample when estimating the statistical significance of the treatment effect. We conducted a series of Monte Carlo simulations to examine the impact of ignoring the matched nature of the propensity-score matched sample on Type I error rates, coverage of confidence intervals, and variance estimation of the treatment effect. We examined estimating differences in means, relative risks, odds ratios, rate ratios from Poisson models, and hazard ratios from Cox regression models. We demonstrated that accounting for the matched nature of the propensity-score matched sample tended to result in type I error rates that were closer to the advertised level compared to when matching was not incorporated into the analyses. Similarly, accounting for the matched nature of the sample tended to result in confidence intervals with coverage rates that were closer to the nominal level, compared to when matching was not taken into account. Finally, accounting for the matched nature of the sample resulted in estimates of standard error that more closely reflected the sampling variability of the treatment effect compared to when matching was not taken into account.

Authors+Show Affiliations

Institute for Clinical Evaluative Sciences, Canada. peter.austin@ices.on.ca

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

20949126

Citation

Austin, Peter C.. "Type I Error Rates, Coverage of Confidence Intervals, and Variance Estimation in Propensity-score Matched Analyses." The International Journal of Biostatistics, vol. 5, no. 1, 2009, pp. Article 13.
Austin PC. Type I error rates, coverage of confidence intervals, and variance estimation in propensity-score matched analyses. Int J Biostat. 2009;5(1):Article 13.
Austin, P. C. (2009). Type I error rates, coverage of confidence intervals, and variance estimation in propensity-score matched analyses. The International Journal of Biostatistics, 5(1), Article 13. https://doi.org/10.2202/1557-4679.1146
Austin PC. Type I Error Rates, Coverage of Confidence Intervals, and Variance Estimation in Propensity-score Matched Analyses. Int J Biostat. 2009 Apr 14;5(1):Article 13. PubMed PMID: 20949126.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Type I error rates, coverage of confidence intervals, and variance estimation in propensity-score matched analyses. A1 - Austin,Peter C, Y1 - 2009/04/14/ PY - 2010/10/16/entrez PY - 2009/1/1/pubmed PY - 2012/7/31/medline KW - coverage KW - matching KW - observational studies KW - propensity score KW - propensity-score matching KW - simulations KW - type I error KW - variance estimation SP - Article 13 EP - Article 13 JF - The international journal of biostatistics JO - Int J Biostat VL - 5 IS - 1 N2 - Propensity-score matching is frequently used in the medical literature to reduce or eliminate the effect of treatment selection bias when estimating the effect of treatments or exposures on outcomes using observational data. In propensity-score matching, pairs of treated and untreated subjects with similar propensity scores are formed. Recent systematic reviews of the use of propensity-score matching found that the large majority of researchers ignore the matched nature of the propensity-score matched sample when estimating the statistical significance of the treatment effect. We conducted a series of Monte Carlo simulations to examine the impact of ignoring the matched nature of the propensity-score matched sample on Type I error rates, coverage of confidence intervals, and variance estimation of the treatment effect. We examined estimating differences in means, relative risks, odds ratios, rate ratios from Poisson models, and hazard ratios from Cox regression models. We demonstrated that accounting for the matched nature of the propensity-score matched sample tended to result in type I error rates that were closer to the advertised level compared to when matching was not incorporated into the analyses. Similarly, accounting for the matched nature of the sample tended to result in confidence intervals with coverage rates that were closer to the nominal level, compared to when matching was not taken into account. Finally, accounting for the matched nature of the sample resulted in estimates of standard error that more closely reflected the sampling variability of the treatment effect compared to when matching was not taken into account. SN - 1557-4679 UR - https://www.unboundmedicine.com/medline/citation/20949126/Type_I_error_rates_coverage_of_confidence_intervals_and_variance_estimation_in_propensity_score_matched_analyses_ L2 - https://www.degruyter.com/doi/10.2202/1557-4679.1146 DB - PRIME DP - Unbound Medicine ER -